Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer

Abstract

The malignant tumors in nature share some common morphological characteristics. Radiomics is not only images but also data; we think that a probability exists in a set of radiomics signatures extracted from CT scan images of one cancer tumor in one specific organ also be utilized for overall survival prediction in different types of cancers in different organs. The retrospective study enrolled four data sets of cancer patients in three different organs (420, 157, 137, and 191 patients for lung 1 training, lung 2 testing, and two external validation set: kidney and head and neck, respectively). In the training set, radiomics features were obtained from CT scan images, and essential features were chosen by LASSO algorithm. Univariable and multivariable analyses were then conducted to find a radiomics signature via Cox proportional hazard regression. The Kaplan-Meier curve was performed based on the risk score. The integrated time-dependent area under the ROC curve (iAUC) was calculated for each predictive model. In the training set, Kaplan-Meier curve classified patients as high or low-risk groups (p-value < 0.001; log-rank test). The risk score of radiomics signature was locked and independently evaluated in the testing set, and two external validation sets showed significant differences (p-value < 0.05; log-rank test). A combined model (radiomics + clinical) showed improved iAUC in lung 1, lung 2, head and neck, and kidney data set are 0.621 (95% CI 0.588, 0.654), 0.736 (95% CI 0.654, 0.819), 0.732 (95% CI 0.655, 0.809), and 0.834 (95% CI 0.722, 0.946), respectively. We believe that CT-based radiomics signatures for predicting overall survival in various cancer sites may exist.

Keywords: Head and neck cancer; Kidney cancer; Lung cancer; Multivariable analysis; Prediction model; Radiomics signature.

Fig. 1

Flow chart of identified CT-based radiomics signatures uniform for overall survival prediction

Fig. 2

Feature selection: top fifteen features selected by LASSO in the training set

Fig. 3

Risk score distribution and ten features radiomics signatures expression in lung 1 training set. A Risk score distribution. B Two categories on the scatter plot: low-risk group (blue color) and high-risk group (red color). C Heat map of 10 radiomics signatures’ expression

Fig. 4

Survival function of risk score generated from CT-based radiomics signatures in A lung 1 training set, B lung 2 testing set, C kidney validation set, and D head and neck validation set

Fig. 5

Time-dependent area under the curves. A Lung 1 training set. B Lung 2 testing set. C Kidney validation set. D Head and neck validation set

Fig. 6

Predictor error curves of multivariable Cox models. A Lung 1 training set. B Lung 2 testing set. C Kidney validation set. D Head and neck validation set`